AI Hardware Acceleration in ASIC Design Lecture 3: AI Acceleration – Systolic Arrays

This lecture dives into the hardware acceleration of AI, specifically focusing on systolic arrays as a key design technique for efficient computation. It contrasts different hardware approaches and establishes that modern AI relies heavily on parallelizable math, making systolic arrays ideal. The lecture details how these arrays function by streaming data through a grid of processing elements, performing multiply-accumulate operations in a pipelined fashion – essentially achieving one computation per clock cycle. The design emphasizes local connections, scalability, and efficient silicon usage, making it well-suited for implementing large neural networks.
The lecture progresses from a basic 2D array design for a simple neural network to demonstrating how it can be scaled to handle massive datasets and complex models. It highlights the benefits of systolic arrays in exploiting the inherent parallelism of AI calculations, and how they overcome limitations of traditional designs. Concepts like sparsity (reducing unnecessary computations) and incorporating more complex functions within processing elements are also touched upon, showcasing how these arrays form the “beating heart” of modern AI hardware acceleration.